Post on 23-May-2020
Applications of Chinese FY series meteorological satellites in boreal forest fire management
AFSC Remote Sensing Workshop
Fairbanks, Alaska, USA. April 5, 2017
Chinese Academy of Forestry, Beijing, China
Center for Forest Disturbance Science, USFS, GA, USA
Fengjun Zhao
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◆ China boreal forest and fire situation
◆ Satellite application in fire management
⚫ FY (FengYun) series
⚫ HJ (HuanJing)series
⚫ GF (GaoFen) series
◆ Potential application to fuel heat properties
◆Conclusions
Outline
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Boreal forest in China◆Location: ⚫ 50º10’~53º33′N⚫ 84,600km2
⚫Helongjiang province, and Inner Mongolia Autonomous Region◆Mainly coniferous forest:⚫ Larch ( Larix gmelinii),⚫Mongolian scotch pine (Pinus sylvestris var. mongolica)⚫ Pinus pumila (shrub species)⚫Fir and spruce
Daxing’anMountain
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Landscape scene and fire situation
This region contributes to 50% burned area in China.
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1987 catastrophic fire in Northeast
May 6th, 1987 ;27 days; 3,325,000 acres burned; 213 people dead; 58,800 fire fighters
NOAA/AVHRR
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Mission Purpose Abbr.DongFangHong Telecommunications and broadcasting
satellitesDFH Series
BeiDou Navigation and positioning satellites BD Series
ShenZhou Experimental spacecraft SZ Series
HaiYang Marine Satellites HY Series
ZiYuan Earth resource satellites ZY series
FengYun Meteorological satellites FY series
HuanJing Environment and disaster monitoring andforecasting
HJ series
GaoFen High resolution Chinese Earth observationsatellite
GF Series
China satellites
AVHRR/NOAA, MODIS/Terra and Aqua, USA6
FY meteorological satellites series
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Polar System Geostationary System
FY
|
1A
1B
1C
1D
FY
|
3A
3B
3C
…
3F
FY
|
2A
2B
2C
2D
2E
Second Generation
First Generation
Second Generation
First Generation
FY
|
4A
4B
4C
…
4FNOAA/AVHRR, Terra and Aqua/MODIS, polar system
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Launched time and Service period
⚫NOAA/AVHRR, 1978.⚫Terra(MODIS), Dec, 18th, 1999.⚫Aqua(MODIS), Mar, 4th,2002.
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No. Products No. Products No. Products
1 Raw Image 10 Rainfall estimation 19 SST
2 Normalized Image 11 Precipitation 20 Snow cover
3 Projected Image 12 AMV 21 Sea ice monitor
4 Mosaic Image 13 Typhoon location 22 Fire spots (24times/day;5km)
5 Cloud classification 14 Upper troposphere humidity
23 Water bodies
6 Total cloud amount 15 Cloud water profile 24 Soil humidity
7 Sand storm detection
16 OLR 25 ISCCP dataset
8 Heavy fog monitor 17 TBB
9 Precipitation index 18 Solar irradiance
FY-2 Operational products
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FY 2C Boreal forest fire monitoring image
Daxing’an Mountain, May, 2006
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No. Products No. Products No. Products
1 Clear Sky Masks 10Downward Long-wave Radiation: Surface
19 Rainfall Rate
2 Cloud Top Height 11Upward Long-wave Radiation: TOA
20 Convective Initiation
3 Cloud Top Temperature 12Upward Long-wave Radiation: Surface
21Tropopause FoldingTurbulence Prediction
4 Cloud Top Pressure 13Reflected Shortwave Radiation: TOA
22 Sea Surface Temperature
5 Cloud Optical Depth 14Downward Shortwave Radiation: Surface
23Fire/Hot Spot (5 minutes; 2km )
6 Cloud Liquid Water 15Legacy Vertical Moisture Profile
24 Land Surface Temperature
7Cloud Particle Size Distribution
16 Ozone Profile & Total 25 Land Surface Emissivity
8Aerosol Detection (including Smoke and Dust)
17 Derived Motion Winds 26 Snow cover
9 Aerosol Optical Depth 18 Lightning Detection 27 Space and Solar products
FY-4 Anticipated products
Similar to the USA's GOES-13/15 3-axis stabilized satellites. 11
Satellites Instrument Primary use
FY 1C/1DMVISR (Multichannel Visible and IR Scanning Radiometer)
Cloud, ice and snow, vegetation, SST, water, heat source, night cloud, water vapor, ocean color, soil humidity, etc.
FY-3 VIRR (Visible and Infrared Radiometer)
Cloud, vegetation, snow and ice, SST, LST, water vapor, aerosol, ocean color, hot spot monitoring, etc.
FY-3MERSI (Medium Resolution Spectral Imager)
True color imagery, cloud, vegetation, snow and ice, ocean color, aerosol, rapid response products (fires, flooding, etc.)
FY 1C/1D,FY-3 operational products
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Sensor parameters comparison
NameSpatial
resolution at nadir (km)
Swath width(km)
Spectral range (µm)
No of Channels/
bands
Global data availability
MVISR-FY-1C/D 1.1 2800 0.58~12.5 10 12 days
VIRR-FY3 1.1 2800 0.43~12.5 10Daily and
hourly
MERSI-FY3 0.25~1.0 2800 0.41 ~ 12.5 20Daily and
hourly
MODIS-NASA 0.25,0.5,1.0 2330 0.4~14.4 36 Daily
AVHRR/3-NOAA 1.1 3000 0.58~12.5 6 Hourly
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FY-1D images for boreal forest fire monitoring
Fire monitoring image Hot spot map
Daxing’an Mountain , May, 28th , 2006
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FY-3 images for fire monitoring and burned area mapping
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Daxing’an Mountain, Oct, 25, 2005
Using FY-3A MERSI, 2009, 250m
FY 3 MERIS Vegetation/fuel type identification
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Relative greenness monitoring
Using FY-3 MERIS 17
Parameters of HJ-1A/B satellites
Platform Payload Spectral range(µm)Spatial
resolution (m)Swath
width(km)Repeat
cycle (days)
HJ-1A
CCD0.43~0.52;0.52~0.600.63~0.69; 0.76~0.90
30 700 4
HSI0.45~0.95(110~128
Bands)100 50 4
HJ-1B
CCD0.43~0.52;0.52~0.600.63~0.69;0.76~0.90
30 700 4
IRS0.75~1.10;1.55~1.753.50~3.90;10.5~12.5
150300
720 4
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The forest fire in Boundary of Inner
Mongolia autonomous region and
Heilongjiang province in June, 2010
Forest fire in Heilongjiang
Province in April, 2009
HJ images for boreal fire monitoring
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GF -1 Parameters
2m/8m Camera 16m Camera
Spectral (µm) Pan: 0.45~0.90;Multi spectrum: 0.45~0.89
Multi spectrum: 0.45~0.89
Resolution(m) Pan:2mMulti spectrum: 8m
16m
Swath (km) 60 (2 camera combined) 800(4 camera combined)
Cycle4 day (with side-look)/42 day (without side-look)
4 day (without side-look)
Application Smoke, burned area mapping
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GF -2 Parameters2m/8m Camera
Spectral Pan: 0.45~0.90µm; Multi spectrum: 0.45~0.89µm
Resolution Pan:1m; Multi spectrum: 4m
Swath 60 km(2 camera combined)
Cycle 5 day (with side-look)/69 day (without side-look)
Application Smoke, burned area mapping
GF -4 Parameters2m/8m Camera
Spectral VNIR Camera: 0.45~0.90µm; NWIR Camera: 3.5~4.1µm
Resolution VNIR(50m); NWIR(400m)
Swath 400km
Cycle 20s
Application Fire monitoring, smoke, burned area mapping
21Note: VNIR(Visible light Near Infrared); MWIR( Medium wave Infrared)
GF-1 images application in burned area mapping
Burned area mapping (a) Using GF-1 WFV image; (b) Using GF-1 PMS image.
22Note: WFV (Wide Field of View); PMS(Panchromatic and MultiSpectral)
AVHRR/NOAA application in China fire monitoring
Morning, May 6, 1987
Afternoon, May 6, 1987
Morning, May 8, 1987
Afternoon, May 20, 1987
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MODIS/NASA application in China fire monitoring
May 28, 2006, Daxing’an Mountain June 3, 2006, Daxing’an Mountain
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Note: The content of volatile-extracts is difficult to measure accurately. However, the fuels have high fatty-extracts always have high volatile-extracts at the same time, for example: conifers, eucalyptus.
Fuel components and heat properties
Component Content (%)Heat
Value (HV) (MJ/kg)
PyrolysisTemperature(℃) Note
Cellulose 38~50 16 220~320 CO, H2, CH4,etc.Hemi-
cellulose7~26 16 320~370
Lignin 23~34 25 200~500Fatty-Extracts
<15 35~40 180~240 Extracts-rich:conifers,eucalyptus,etc.
Volatile-Extracts
32 155~175
Mineral <1
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Fatty-extracts from different species
Zhao, FJ, et al. Supercritical extracts of forest fuels in Great Xing’an Mountains. Journal of Forestry Research,2016,27(5):1143-1151
Instrument: Supercritical fluid CO2 extraction (SFE)
⚫Pressure: 40 Mpa
⚫Temperature:
45℃
⚫ Time: 80 min
⚫CO2 flow :
2.0 L/min.
⚫Sample size:
60 mesh
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48 samples (12 coniferous twigs and needles, 30 hardwood tree and shrub leaves and twigs, 6 herbaceous species)
12 samples (6 needles, 6 conifers twigs)
6 needle samples
Extract – heating relationships
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Cone Calorimeter Test
Time from start of test (s)
Schima mass loss rate
Pine mass loss rate
CO2 yield of Schima
CO2 yield of pine
0
0.03
0.06
0.09
0.12
0.15
50 100 150 200 250 300
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Sample mass loss rate
(g/s
)
Carbon dioxide yield
(kg
/kg
)
The experiments made us a deep understanding on the combustion
difference of the varied tree species.
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Extract-rich species- coniferous tree species
Larch, Daxing’an Mountain Scotch pine, Daxing’an Mountain
Black spruce, Alaska Douglas fir, USA29
Extract-rich species- others
Pinus pumila, shrub, China Ledum palustre, shrub, China
Eucalyptus forest-Australia Rosemary Mediterranean coast 30
Vegetation classification of China (MODIS MCD12Q1: 2012)
Vegetation/fuel classification:⚫ Spectral information⚫ Phenology
Spectral information⚫Chlorophyll content⚫Water content
Extract contents have not been considered.
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Oct 17th,2016 Nov 14th,2016
Extract-rich species: Fraser fir(Abies fraseri), Red spruce (Picea rubens), Pitch pine
(Pinus rigida), White pine (Pinus strobus), Virginia or scrub pine (Pinus virginiana)
2016 Southern Appalachian Wildfires: Fuel, Emissions, and Smoke Impacts
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Satellite potential application to fuel heat properties
◆Boreal forest are mainly
composed by exacts rich tree
species, such as, fir, spruce, larch,
etc, and exacts rich shrub species,
such as, Ledum palustre, etc.
◆In the future, if the exacts
content of vegetation can be
detected and monitored by RS. It
will be very useful for fire
management in boreal forest!
Crowning fire in a black spruce stand during the lightning-ignited, 2004 Taylor Complex Wildfire in southeastern interior Alaska.
https://www.fs.fed.us/database/feis/fire_regimes/AK_black_spruce/all.html
High fire intensity and more emission, especially black carbon
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Satellites development trends in China
⚫GF-5, GF-6,GF-7
⚫A number of geo-science satellites and
small satellites
FY-4A, Dec 11, 2016
By 2020, planned to
launch:
⚫FY 4B, FY-3F, FY-
4C, FY-RM2, FY-3G,
FY-4M
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Conclusions
➢Chinese satellites have been important part of the fire
management. FY,HJ,GF series and other satellites did good job on
fire detection, monitoring, burned area mapping, etc.
➢However improvements are needed in, such as, spectrum
resolution and analysis technologies to increase the capacities of
detecting fuel extract contents and the impacts on heat release and
fire emission.
➢Conduct more comparisons between FY and other satellites,
such as Terra and Aqua, and produce more fire products from FY.
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Acknowledgement ⚫Chinese Academy of Forestry / Institute of Forest Ecology, Environment and Protection; China State Forestry Administration ; Chinese Scholarship Council
⚫Alaska Fire Science Consortium⚫Center for Forest Disturbance Science, Forest Service; USDA Forest Service International Program
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